1. Field of the Invention
The invention relates to a method and system for the computerized automatic detection of gross abnormalities and asymmetries in chest images, and more specifically to a method and system for detection in digital chest radiographs. Asymmetries are detected by multiple stages of global and local gray-level thresholding along with contour detection. Abnormalities are detected based on deviation from expected symmetries between the left and right lungs, using such features as size and density of the aerated lung regions.
2. Discussion of the Background
Computer-aided diagnosis (CAD) has potential to become a valuable tool for detecting subtle abnormalities in chest radiographs. It would be useful for a CAD scheme to detect more large-scale abnormalities, which commonly cause abnormal asymmetry on the radiograph. In general, asymmetric abnormalities appear as a substantial decrease in the area of aerated lung in one hemithorax. This would include interstitial infiltrates, dense air space infiltrates, pleural effusions, large masses, or unilateral emphysema.
Most CAD schemes currently employed in digital chest radiography are specific to one particular and often localized pathology, for example lung nodule, interstitial infiltrate, pneumothorax or cardiomegaly. These schemes often utilize a priori information regarding the "normal" appearance of the ribcage, diaphragm and mediastinum in a digital chest radiograph. A potential problem arises when the nature of the thoracic abnormality is such that it substantially affects the volume of the lungs. An abnormality of this type will usually cause a decrease in the aerated lung region (i.e. the high optical density associated with the normally low attenuation of the lungs) as projected onto the radiograph. This can substantially alter the overall morphology of the chest, resulting in potential failure of such CAD schemes. Detection of these abnormalities may also prove useful in prioritizing abnormal cases in a picture archiving and communication system (PACS).
The present application discloses a technique for the automated detection of abnormal asymmetry in digital chest radiographs. The method consists of an iterative global thresholding technique in conjunction with a contour detection algorithm to construct an initial set of contours around the two projected aerated lung regions in a chest image. In order to identify the lungs more accurately, a local thresholding technique is then applied within regions of interest (ROIs) centered along the contours that result from the global thresholding. The areas and densities of the two lung regions identified in this manner can be compared in order to determine whether an asymmetric abnormality is present.